from os import listdir, path
import numpy as np
import scipy, cv2, os, sys, argparse
import dlib, json, subprocess
from tqdm import tqdm
from glob import glob
import torch

import ..audio
import ..face_detection
from ..models import Wav2Lip

parser = argparse.ArgumentParser(description='Code to generate results for test filelists')

parser.add_argument('--filelist', type=str, 
					help='Filepath of filelist file to read', required=True)
parser.add_argument('--results_dir', type=str, help='Folder to save all results into', 
									required=True)
parser.add_argument('--data_root', type=str, required=True)
parser.add_argument('--checkpoint_path', type=str, 
					help='Name of saved checkpoint to load weights from', required=True)

parser.add_argument('--pads', nargs='+', type=int, default=[0, 0, 0, 0], 
					help='Padding (top, bottom, left, right)')
parser.add_argument('--face_det_batch_size', type=int, 
					help='Single GPU batch size for face detection', default=64)
parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip', default=128)

# parser.add_argument('--resize_factor', default=1, type=int)

args = parser.parse_args()
args.img_size = 96

def get_smoothened_boxes(boxes, T):
	for i in range(len(boxes)):
		if i + T > len(boxes):
			window = boxes[len(boxes) - T:]
		else:
			window = boxes[i : i + T]
		boxes[i] = np.mean(window, axis=0)
	return boxes

def face_detect(images):
	batch_size = args.face_det_batch_size
	
	while 1:
		predictions = []
		try:
			for i in range(0, len(images), batch_size):
				predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size])))
		except RuntimeError:
			if batch_size == 1:
				raise RuntimeError('Image too big to run face detection on GPU')
			batch_size //= 2
			args.face_det_batch_size = batch_size
			print('Recovering from OOM error; New batch size: {}'.format(batch_size))
			continue
		break

	results = []
	pady1, pady2, padx1, padx2 = args.pads
	for rect, image in zip(predictions, images):
		if rect is None:
			raise ValueError('Face not detected!')

		y1 = max(0, rect[1] - pady1)
		y2 = min(image.shape[0], rect[3] + pady2)
		x1 = max(0, rect[0] - padx1)
		x2 = min(image.shape[1], rect[2] + padx2)
		
		results.append([x1, y1, x2, y2])

	boxes = get_smoothened_boxes(np.array(results), T=5)
	results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2), True] for image, (x1, y1, x2, y2) in zip(images, boxes)]

	return results 

def datagen(frames, face_det_results, mels):
	img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []

	for i, m in enumerate(mels):
		if i >= len(frames): raise ValueError('Equal or less lengths only')

		frame_to_save = frames[i].copy()
		face, coords, valid_frame = face_det_results[i].copy()
		if not valid_frame:
			continue

		face = cv2.resize(face, (args.img_size, args.img_size))
			
		img_batch.append(face)
		mel_batch.append(m)
		frame_batch.append(frame_to_save)
		coords_batch.append(coords)

		if len(img_batch) >= args.wav2lip_batch_size:
			img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)

			img_masked = img_batch.copy()
			img_masked[:, args.img_size//2:] = 0

			img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
			mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])

			yield img_batch, mel_batch, frame_batch, coords_batch
			img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []

	if len(img_batch) > 0:
		img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)

		img_masked = img_batch.copy()
		img_masked[:, args.img_size//2:] = 0

		img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
		mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])

		yield img_batch, mel_batch, frame_batch, coords_batch

fps = 25
mel_step_size = 16
mel_idx_multiplier = 80./fps
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('Using {} for inference.'.format(device))

detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, 
											flip_input=False, device=device)

def _load(checkpoint_path):
	if device == 'cuda':
		checkpoint = torch.load(checkpoint_path)
	else:
		checkpoint = torch.load(checkpoint_path,
								map_location=lambda storage, loc: storage)
	return checkpoint

def load_model(path):
	model = Wav2Lip()
	print("Load checkpoint from: {}".format(path))
	checkpoint = _load(path)
	s = checkpoint["state_dict"]
	new_s = {}
	for k, v in s.items():
		new_s[k.replace('module.', '')] = v
	model.load_state_dict(new_s)

	model = model.to(device)
	return model.eval()

model = load_model(args.checkpoint_path)

def main():
	assert args.data_root is not None
	data_root = args.data_root

	if not os.path.isdir(args.results_dir): os.makedirs(args.results_dir)

	with open(args.filelist, 'r') as filelist:
		lines = filelist.readlines()

	for idx, line in enumerate(tqdm(lines)):
		audio_src, video = line.strip().split()

		audio_src = os.path.join(data_root, audio_src) + '.mp4'
		video = os.path.join(data_root, video) + '.mp4'

		command = 'ffmpeg -loglevel panic -y -i {} -strict -2 {}'.format(audio_src, '../temp/temp.wav')
		subprocess.call(command, shell=True)
		temp_audio = '../temp/temp.wav'

		wav = audio.load_wav(temp_audio, 16000)
		mel = audio.melspectrogram(wav)
		if np.isnan(mel.reshape(-1)).sum() > 0:
			continue

		mel_chunks = []
		i = 0
		while 1:
			start_idx = int(i * mel_idx_multiplier)
			if start_idx + mel_step_size > len(mel[0]):
				break
			mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
			i += 1

		video_stream = cv2.VideoCapture(video)
			
		full_frames = []
		while 1:
			still_reading, frame = video_stream.read()
			if not still_reading or len(full_frames) > len(mel_chunks):
				video_stream.release()
				break
			full_frames.append(frame)

		if len(full_frames) < len(mel_chunks):
			continue

		full_frames = full_frames[:len(mel_chunks)]

		try:
			face_det_results = face_detect(full_frames.copy())
		except ValueError as e:
			continue

		batch_size = args.wav2lip_batch_size
		gen = datagen(full_frames.copy(), face_det_results, mel_chunks)

		for i, (img_batch, mel_batch, frames, coords) in enumerate(gen):
			if i == 0:
				frame_h, frame_w = full_frames[0].shape[:-1]
				out = cv2.VideoWriter('../temp/result.avi', 
								cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))

			img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
			mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)

			with torch.no_grad():
				pred = model(mel_batch, img_batch)
					

			pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
			
			for pl, f, c in zip(pred, frames, coords):
				y1, y2, x1, x2 = c
				pl = cv2.resize(pl.astype(np.uint8), (x2 - x1, y2 - y1))
				f[y1:y2, x1:x2] = pl
				out.write(f)

		out.release()

		vid = os.path.join(args.results_dir, '{}.mp4'.format(idx))

		command = 'ffmpeg -loglevel panic -y -i {} -i {} -strict -2 -q:v 1 {}'.format(temp_audio, 
								'../temp/result.avi', vid)
		subprocess.call(command, shell=True)

if __name__ == '__main__':
	main()
